Chutes Releases One-Year LLM Serving Workload Study: Unveiling Workload Evolution in Real-World Production Environments

Summary:The Chutes team has released a comprehensive study on LLM serving workloads, analyzing a one-year trace of real-world production data to uncover the evolution of workloads and user-model interaction patterns. This study provides the first large-scale, multi-model, and multi-user production behavior dataset, encompassing both popular and long-tail models. The analysis spans aggregate, temporal, model-level, and user-level perspectives, offering valuable insights for benchmarking and optimizing LL

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Background and Significance

As Large Language Models (LLMs) are increasingly applied across various domains, understanding the workload characteristics of LLM services has become a critical research topic in cloud computing. However, existing studies are often limited to short time windows or single models, lacking a comprehensive analysis of multi-model, multi-user interactions in real-world production environments. The Chutes team's study fills this gap by analyzing a one-year production trace, revealing the evolution of LLM serving workloads and user-model interaction patterns.

Key Research Content

1. Data Collection and Analysis Methods

   • Collected real production data from multiple models and users, including both popular and long-tail models.
   • Employed a four-dimensional analysis framework: aggregate, temporal, model-level, and user-level, to comprehensively uncover workload characteristics.

2. Key Findings

   • Workload Evolution: LLM service workloads show significant variation over time, with increasing usage frequency and resource demands for popular models.
   • User-Model Interaction Patterns: Different users exhibit distinct usage patterns, and while long-tail models have smaller user bases, they contribute non-negligible traffic.
   • Resource Demands: The complexity of models and the diversity of user requests significantly impact resource demands, suggesting the need for more refined resource scheduling strategies.

3. Data Openness and Future Research

   • The research team will release the full one-year trace, providing valuable resources for subsequent studies.
   • Encourages the community to conduct more research on LLM service system optimization, workload prediction, and resource scheduling strategies based on this dataset.

Technical Highlights

• Multi-Dimensional Analysis Framework: The first study to employ a four-dimensional analysis framework (aggregate, temporal, model-level, and user-level) to comprehensively reveal LLM serving workload characteristics.
• Real Production Data: Data sourced from real production environments, avoiding the limitations of simulated or short-term data.
• Open Dataset: The release of the full one-year trace provides a valuable resource for future research.

Industry Impact and Developer Recommendations

• Industry Impact: The study provides important insights for the design and optimization of LLM service systems, helping to improve service quality and resource utilization.
• Developer Recommendations: Developers should pay attention to the evolution of LLM service workloads and adjust service strategies based on user-model interaction patterns. Additionally, leveraging the open dataset for further research and testing is recommended.

Conclusion

The Chutes team's study offers a new perspective and methodology for analyzing LLM serving workloads, laying the foundation for future research. As LLM technology continues to evolve, such in-depth studies will become increasingly important.

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Tags: #LLMs & Foundation Models #Cloud Computing #Workload Analysis #Data Openness #AI Research